About Us

The educational technology collective is a group of students, staff, and faculty who explore the intersections of technology with teaching, learning, and education, with a particular focus on learning analytics, educational data mining, and collaborative engagement. We’re interdisciplinary, and include researchers with backgrounds in computer science, information, psychology, statistics, and more.

Interested in hearing more about what we do? Check out the projects listed below to see where you might fit, and contact the lead researcher to see what opportunities might exist for volunteering, part time work, or independent study. Or browse our github for current public codebases, or scholarly publications below, to get a sense of how we’re trying to build better education.

Pheobe Liang

Projects

Understanding the nuances of a student’s education requires looking beyond single metrics and datasets. Despite being driven by a common goal of using data-driven inquiry to positively affect teaching and learning in higher education, our methods, tools, and data are different. As a result, we aim to tackle these issues by connecting various forms of knowledge representations from a variety of disciplinary methodologies to construct a holistic model of education.

Some examples of projects we’re working on include: building explanatory models to identify underlying causes of success for next-generation intervention systems and creating infrastructure for querying indicators of student achievement across disparate datasets in order to facilitate research.

The Mentor Academy is a data-science learning community of practice where students volunteer their knowledge, skills, and time to give back to new students. Built around the content of the Applied Data Science with Python online course on Coursera, students are able to give back by creating new culturally relevant problem sets for online learners or engaging in mentorship in discussion forums.The first cohort of mentors for the Mentor Academy were recruited in October 2017. The next iteration of the academy is planned for Summer 2018.Project leads: Anant Mittal and Christopher Brooks

Replication in Educational Models

Whether machine-learned, human generated, or a hybrid, models of student success in education need to be replicated in new contexts and datasets in order to ensure generalizability . We’re building the software to do this, and hooking it up to large datasets of hundreds of classes with millions of learners through collaboration between the University of Michigan and the University of Pennsylvania.

The rapid growth of social media and online communities has dramatically changed the manner in which communication takes place, and most people engage in some form of online asynchronous or synchronous conversation every day. As a result, communication and collaboration are key skills across all aspects of modern life, from learning and working to our political and social life more broadly. In the Educational Technology Collective, we are working to gain a deeper understanding of online discourse and group dynamics in order facilitate improved educational technologies, wider world access to learning, and more competent and successful citizens. Towards this effort, we have several projects that focus on using language and discourse to uncover the dynamics of socially significant, cognitive, and affective processes in a variety of online educational interactions, including small group computer-mediated collaborative learning environments, and massive open online courses (MOOCs).

Project leads: Nia Dowell and Christopher Brooks

Predicting the Academic Success of Students

Our work focuses on nurturing the development of self-regulated learning (SRL) skills in students. Self­regulation is an important feature of engaged learners: those who have the ability, willingness, and experience to reflect on how their behaviors relate to learning outcomes. By predicting the academic success of students (PASS) and revealing how that success correlates with their activities, we aim to help learners engage in conversations with themselves, their peers, and their instructors to improve learning practices and outcomes.

In collaboration with the Information Quest team, we’re building supervised machine learning models and additional infrastructure to achieve these goals and bridge the gap between research and production. We aim to support the work of researchers and tool developers who want to make use of predictive models, and to impact the broader landscape of higher education through partnerships such as the Unizin Consortium.